Bottom Line:
We found that n-spike bursts from both models transmit information by modulating their spike count in response to changes to instantaneous input features, such as slope, phase, amplitude, etc.Most importantly, the neural code employed by the simple and the biologically realistic models was largely the same, implying that the simple thalamic neuron model contains the essential ingredients that account for the computational properties of the thalamic burst code.Thus, our results suggest the n-spike burst code is a general property of thalamic neurons.

Affiliation: Faculty of Life Sciences, The University of Manchester Manchester, UK.

ABSTRACTThalamic neurons have been long assumed to fire in tonic mode during perceptive states, and in burst mode during sleep and unconsciousness. However, recent evidence suggests that bursts may also be relevant in the encoding of sensory information. Here, we explore the neural code of such thalamic bursts. In order to assess whether the burst code is generic or whether it depends on the detailed properties of each bursting neuron, we analyzed two neuron models incorporating different levels of biological detail. One of the models contained no information of the biophysical processes entailed in spike generation, and described neuron activity at a phenomenological level. The second model represented the evolution of the individual ionic conductances involved in spiking and bursting, and required a large number of parameters. We analyzed the models' input selectivity using reverse correlation methods and information theory. We found that n-spike bursts from both models transmit information by modulating their spike count in response to changes to instantaneous input features, such as slope, phase, amplitude, etc. The stimulus feature that is most efficiently encoded by bursts, however, need not coincide with one of such classical features. We therefore searched for the optimal feature among all those that could be expressed as a linear transformation of the time-dependent input current. We found that bursting neurons transmitted 6 times more information about such more general features. The relevant events in the stimulus were located in a time window spanning ~100 ms before and ~20 ms after burst onset. Most importantly, the neural code employed by the simple and the biologically realistic models was largely the same, implying that the simple thalamic neuron model contains the essential ingredients that account for the computational properties of the thalamic burst code. Thus, our results suggest the n-spike burst code is a general property of thalamic neurons.

Figure 8: Dependence of the encoded information on the location of the window used to define the stimulus. (A) Explanatory diagrams illustrating the definition of the windows (A1), and the subdivision of maps (A2) into regions: Stimuli may be taken entirely before (A2 a), after (A2 b), or astride (A2 c) burst onset (T = 0). Vertical dashed black lines mark Tstart = 0 ms and horizontal lines represent Tend = 0 ms. (B, C) Information between n and V1-projected stimuli is color-plotted as a function of the location of the times Tstart and Tend of the window, for the MC (B) and the IFB models (C). Models were driven with stimuli with σOU = 1μA, τOU = 5 ms, μOU = 0μA. Information is calculated with digitization of M = 32 bins and shuffle-corrected to account for bias.

Mentions:
The directions obtained with MDA, and consequently, the information values derived from projecting the stimulus on the first MDA vector, depend on the location of the time window used to represent the stimuli. In order to determine the optimal window, we now repeat the analysis, systematically sweeping the location of the first and last times defining the window, Tstart and Tend. For each window, we project the stimulus on the obtained optimal axis, and calculate the mutual information, as shown in Figure 7A1. The maps are displayed in half planes because by definition, Tstart < Tend. Figure 8A2 shows a diagrammatic representation, showing that all possible windows can be divided into 3 regions. Windows in region (a) are entirely located before burst onset, since even Tend < 0. Windows in region (b) are entirely located after burst onset, since even Tstart > 0. Windows in region (c) are astride burst onset, since Tstart < 0 and Tend > 0.

Figure 8: Dependence of the encoded information on the location of the window used to define the stimulus. (A) Explanatory diagrams illustrating the definition of the windows (A1), and the subdivision of maps (A2) into regions: Stimuli may be taken entirely before (A2 a), after (A2 b), or astride (A2 c) burst onset (T = 0). Vertical dashed black lines mark Tstart = 0 ms and horizontal lines represent Tend = 0 ms. (B, C) Information between n and V1-projected stimuli is color-plotted as a function of the location of the times Tstart and Tend of the window, for the MC (B) and the IFB models (C). Models were driven with stimuli with σOU = 1μA, τOU = 5 ms, μOU = 0μA. Information is calculated with digitization of M = 32 bins and shuffle-corrected to account for bias.

Mentions:
The directions obtained with MDA, and consequently, the information values derived from projecting the stimulus on the first MDA vector, depend on the location of the time window used to represent the stimuli. In order to determine the optimal window, we now repeat the analysis, systematically sweeping the location of the first and last times defining the window, Tstart and Tend. For each window, we project the stimulus on the obtained optimal axis, and calculate the mutual information, as shown in Figure 7A1. The maps are displayed in half planes because by definition, Tstart < Tend. Figure 8A2 shows a diagrammatic representation, showing that all possible windows can be divided into 3 regions. Windows in region (a) are entirely located before burst onset, since even Tend < 0. Windows in region (b) are entirely located after burst onset, since even Tstart > 0. Windows in region (c) are astride burst onset, since Tstart < 0 and Tend > 0.

Bottom Line:
We found that n-spike bursts from both models transmit information by modulating their spike count in response to changes to instantaneous input features, such as slope, phase, amplitude, etc.Most importantly, the neural code employed by the simple and the biologically realistic models was largely the same, implying that the simple thalamic neuron model contains the essential ingredients that account for the computational properties of the thalamic burst code.Thus, our results suggest the n-spike burst code is a general property of thalamic neurons.

Affiliation:
Faculty of Life Sciences, The University of Manchester Manchester, UK.

ABSTRACTThalamic neurons have been long assumed to fire in tonic mode during perceptive states, and in burst mode during sleep and unconsciousness. However, recent evidence suggests that bursts may also be relevant in the encoding of sensory information. Here, we explore the neural code of such thalamic bursts. In order to assess whether the burst code is generic or whether it depends on the detailed properties of each bursting neuron, we analyzed two neuron models incorporating different levels of biological detail. One of the models contained no information of the biophysical processes entailed in spike generation, and described neuron activity at a phenomenological level. The second model represented the evolution of the individual ionic conductances involved in spiking and bursting, and required a large number of parameters. We analyzed the models' input selectivity using reverse correlation methods and information theory. We found that n-spike bursts from both models transmit information by modulating their spike count in response to changes to instantaneous input features, such as slope, phase, amplitude, etc. The stimulus feature that is most efficiently encoded by bursts, however, need not coincide with one of such classical features. We therefore searched for the optimal feature among all those that could be expressed as a linear transformation of the time-dependent input current. We found that bursting neurons transmitted 6 times more information about such more general features. The relevant events in the stimulus were located in a time window spanning ~100 ms before and ~20 ms after burst onset. Most importantly, the neural code employed by the simple and the biologically realistic models was largely the same, implying that the simple thalamic neuron model contains the essential ingredients that account for the computational properties of the thalamic burst code. Thus, our results suggest the n-spike burst code is a general property of thalamic neurons.